LANGCHAIN — Incorporating Domain Specific Knowledge in SQL LLM Solutions
The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it. — Mark Weiser
In this tutorial, we will explore how to extend the standard SQL Toolkit provided by LangChain with domain-specific knowledge to enhance the capabilities of an LLM-SQL solution. We will cover how to address common problems encountered when using generic tools and demonstrate how to incorporate custom tools to leverage domain-specific knowledge.
The Problems
When using the standard SQL Toolkit, users may encounter problems such as incorrect query generation, inefficient thinking process, and prompts with irrelevant information. These issues arise from a lack of leveraging domain-specific knowledge about the databases.
Extending the SQL Toolkit
We can use Retrieval Augmented Generation (RAG) techniques to dynamically include domain-specific knowledge into the prompt, addressing the aforementioned problems. Let’s explore some examples of how to extend the standard SQL Toolkit with custom tools.
Including few shot examples
Feeding the prompt with few-shot examples of question-query matches improves query generation accuracy. By dynamically including relevant examples, we can guide the agent on how to build queries based on questions.
For example:
agent.run("How many employees do we have?")Finding misspellings in proper nouns
To make the system robust to misspellings, we can create a vector store using all the distinct proper nouns in the database. The agent can query this vector store to find the correct spelling for a word when a user includes a proper noun in their question.
For example:
agent.run("What is 'Francis Trembling's email address?")Going further
To further enhance the few-shot approach, we can consider applying a similarity threshold to decide whether retrieved examples are related enough to be included in the prompt, setting a threshold to decide if the examples are far too related and prioritizing diversity of the few-shot examples.
Additionally, we can retrieve all values from a relevant categoric column if the user’s question involves filtering a column and adjust sample rows to show only the columns that are relevant to the user question.
By leveraging these domain-specific tools, we can significantly improve the accuracy and efficiency of the LLM-SQL solution.
In conclusion, by extending the standard SQL Toolkit with domain-specific knowledge, we can address common problems encountered when building custom LLM-SQL solutions and significantly enhance the capabilities of the agent. If you want to contribute to implementing these best practices or have other helpful insights, join the discussion in the #sql channel in Discord!
This tutorial has covered how to incorporate domain-specific knowledge in SQL LLM solutions using the LangChain library. By leveraging RAG techniques and custom tools, we can significantly enhance the capabilities of the standard SQL Toolkit and improve the accuracy and efficiency of LLM-SQL solutions.